似然比檢測 的英文怎麼說

中文拼音 [sìránbǐjiǎncè]
似然比檢測 英文
likelihood ratio test
  • 然 : Ⅰ形容詞（對; 不錯） right; correct Ⅱ代詞（如此; 這樣; 那樣） so; like that Ⅲ連詞[書面語] （然而）...
  • 比 : Ⅰ動詞1 （比較; 較量高下、 長短、距離、好壞等） compare; compete; contrast; match; emulate 2 （比...
  • 檢 : Ⅰ動詞1 （查） check up; inspect; examine 2 （約束; 檢點） restrain oneself; be careful in one s c...
  • 測 : 動詞1. （測量） survey; fathom; measure 2. （測度; 推測） conjecture; infer
  • 檢測 : check; detection; test; gauging; detecting; sensing; [工業] checkout; measuring
  1. Orientation weighting restricts the direction of trajectories by predicted candidate trajectories and shrink ferret window, and reduces the amount of calculation. truncated sequential probability ratio test ( tsprt ) is also a algorithm of tbd, it can improve calculation efficiency by multistage thresholds to truncate tree - structured list of candidate trajectory of low degree of confidence

    截斷序貫似然比方法也是一種tbd檢測的方法，其通過多級門限截斷置信度較低的軌跡樹達到提高效率的目的，在動態規劃中加入該演算法，使之積累的軌跡數減少，降低了計算量和存儲量。
  2. In this paper, we emphasis on the distributed mimo zero forcing detection, maximum likelihood detection and minimum mean square error detection. the simulation results are presented to compare the proposed distributed mimo detection algorithms

    本文重點闡述了分佈mimo的迫零檢測演算法、最大似然檢測演算法和最小均方誤差檢測演算法，並通過模擬比較了這三種分佈mimo檢測演算法的性能。
  3. Likelihood ratio test and power analysis of repeated measures models

    重復測量試驗模型參數似然比檢驗及其功效分析
  4. The main results in this paper as follows : first, a prototype of a shape - based image database retrieval system is completed, it can receive the query mode both by giving an example image and by sketching the desired object on the screen, second, five shape - based image retrieval methods are realized ; third, an algorithm based on triangulation for shape - based image retrieval is brought forward. in this algorithm, firstly, the edge of the original image is followed and the candidate corners in the original image are detected. then the counterpoints of the candidate corners in the result of edge follow are found, and the boundary corners whose counterpoints have been found are queued in the order of their counterpoints in edge follow

    同時本文中主要完成了以下三方面的工作：完成了一個基於形狀特徵的圖象檢索的原型系統，可實現例子圖象或徒手繪草圖兩種查詢方式；實現了五種利用形狀特徵進行圖象檢索的具體方法，並對其進行了分析比較；並且在繼承將三角剖分引入形狀檢索的思想基礎上提出了利用三角剖分進行形狀檢索的一種新演算法：先對原圖象進行邊界跟蹤和角點檢測；然後尋找初始角點在邊界跟蹤中的對應點，並對找到對應點的角點按其對應點在邊界跟蹤中的順序進行排序；再對排序后的角點進行德洛內三角剖分，得到能表示目標真實形狀的三角形序列；最後計算三角形序列的角度直方圖作為形狀特徵，並進行相似性匹配。
  5. This feature perfectly combine the frequency in acoustics level and the temperament in music semantic level, we use the cosine distance of this feature to represent the similarity of two music clips, then we design a group of algorithms that is inspired from the thought of edit distance and dynamic programming. they segment the feature vectors into groups at first, then through group similarity match, group recurrent detect, merge recurrent group and structure label joined algorithms to complete the music structure label task. because this is a really new field of research and no good method of evaluation had been finding, we propose a new evaluation method and the results of the experiments show that it is a good method

    然後設計了一組源於編輯距離和動態規劃思想的音樂結構分析演算法，首先將特徵向量分組，然後經過組相似匹配、組重現檢測、重現組歸並和自動標注四個前後銜接的環節實現了音樂結構的自動標注，較好地實現了將音頻形式的音樂自動標注為表示音樂結構的三元組列表形式，由於這是一個新的領域，目前還沒有比較好的量化評價方法，本文提出一種新的評價方法，並用它來評價結構分析的結果，取得了較好的效果。
  6. Simulation results for a numerical example show preliminarily that this method for estimating spreading time of tsd channel with a proposed multi - hypothesis rci is simple, convenient, and feasible. then the experiment is carried through in tank for verifying the results including detection capability of src and rci and feasibility of proposed multi - hypothesis rci for estimating spreading time of tsd

    接著進行水池實驗驗證了ffd和tsd通道的最佳似然比檢測器分別為src和rci ，並針對實驗環境對多重假設的rci演算法作了改進，估計了tsd通道的擴展時間。
  7. Firstly, we directly use the motion vectors of macro - blocks defined in mpeg - i / ii compressing standards and filter the immobile macro - blocks. then, we build a skin color model in ycbcr color space using the convergent property of skin color, and we present the gaussian model skin recognition method and positive - negative look - up table method in details. and we analyze the texture of skin after wavelet transform and present a bayesian method based texture recognition method and a high texture filtering method

    根據皮膚的運動性，首先直接利用mpeg -中的壓縮標準中有關宏塊運動預測的方法，提取宏塊的運動矢量，將沒有運動的宏塊過濾掉；然後，利用皮膚顏色的聚合性，在ycbcr顏色空間建立了皮膚的顏色模型，並分別闡述了基於高斯分佈模型的皮膚檢測法和正反概率表方法；最後，通過對皮膚進行小波變換后的紋理進行統計后，發現有效的利用皮膚紋理特徵，可以比較有效的過濾掉那些具有類似於皮膚顏色的背景，分別闡述了基於貝葉斯方法的紋理檢測方法和高紋理過濾法。
  8. We show the difficulty in hardware implementation by complexity analysis, then based on the analysis we provide our scheme, which has low complexity and has been proved to be equivalent to the mmse algorithm without iterations. we provide a scheme called the mmse - llr ( logarithm likelihood ratio ) which is the simplified llr demodulation scheme and omitted a lot of non - linear operations

    然後提出自己的簡化方案，即通過簡要的推導證明在無循環的情況下mmsesic檢測演算法完全等同於mmse檢測演算法，結合運算的特點提出了簡化的對數似然比解調方案，從而省去了大量的非線性運算，簡化后的方案在文中被稱為mmse - llr檢測方案。
  9. Applications of multiple - model smoothing algorithms for maneuvering target tracking are studied via simulation, some important conclusions are obtained. based on model - set sequential likelihood ratio, an enhanced agimm, in which model - set adaptation is implemented by jointly utilizing model posterior probability and predication probability, is proposed, simulation results indicate that improvements of both dynamic and steady state tracking performance are achieved with the enhanced algorithm

    模擬研究了多模型平滑演算法在機動目標跟蹤中的應用；利用模型集合序貫似然比檢驗，提出了一種綜合利用模型后驗概率和預測概率實現模型集合自適應的綜合格自適應多模型演算法，模擬實驗表明演算法有效改善了動態跟蹤精度和穩態跟蹤性能。
  10. Compare a lot of face image characteristic vector with face image sets characteristic matrix in order to get their similarity, and find the least value of similarity as threshold. in the detecting phase, compute the similarity between characteristic vector of testing region in gray image and face image sets characteristic matrix, if the similarity bigger or equal to threshold then the testing region is a human face, otherwise is not

    然後，用大量的人臉圖像的特徵向量與人臉圖像集特徵矩陣比較它們的相似程度，找出值小相似度，並把這個最小相似度作為閾值；在檢測階段，求出灰度圖像的待測區域的特徵向量與人臉特徵矩陣的相似度，若該相似度大於等於閾值，則是人臉，否則不是人臉。
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